研究问题的奇思妙想:在学习型医疗系统中让患者参与长期 COVID 研究的优先排序

IF 2.6 Q2 HEALTH POLICY & SERVICES Learning Health Systems Pub Date : 2024-02-28 DOI:10.1002/lrh2.10410
Ann Blair Kennedy, Ariana Mitcham, Katherine Parris, Faith Albertson, Luis Sanchez Ferrer, Conor O'Boyle, Maushmi K. Patel, Tracey Gartner, Amy M. Broomer, Evan Katzman, Jeanette Coffin, Jennifer T. Grier, Nabil Natafgi
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引用次数: 0

摘要

背景 在学习型医疗系统中,研究工作不可或缺的一个组成部分就是让患者参与研究过程的各个阶段。虽然在预先确定的研究问题上有明确的患者参与最佳实践,但在研究问题的形成和优先排序阶段,几乎没有让患者参与的具体方法。此外,对于长COVID这样的新兴疾病,还没有针对特定人群的有意义参与策略。 方法 COVID-19 聚焦虚拟患者参与工作室(CoVIP studio)是一个虚拟小组,旨在促进围绕长期 COVID("Long COVID")(又称急性 SARS-CoV-2 后综合征(PASC))的影响开展以患者为中心的研究。我们招募了一组不同的专家组成员,并对他们进行了有关研究和长期 COVID 的多个不同领域的知识、能力和才干的培训。研究人员制定了一个三步骤方法,包括记录小组成员的广泛疑问,以提出针对患者的研究问题。 结果 对专家组成员培训课程中讨论的 "疑惑 "进行了分析,以确定特定人群、干预措施、比较者、结果和时间范围(PICOT)要素,然后利用这些要素创建了一项调查,以确定对专家组成员最重要的要素。根据调查结果,使用 PICOT 格式制定了 10 个研究问题。然后,专家小组成员根据认为的重要程度对这些问题进行排序,并在第二次调查中将 100 万虚构赠款分配给所选的五个问题。通过这种按部就班的优先排序过程,项目小组成功地将小组成员的研究疑问转化为可调查的研究问题。 结论 这种方法对于在 Long COVID 研究范围内以及在其他罕见病或新发疾病的研究中推动患者参与的优先排序工作具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Wonderings to research questions: Engaging patients in long COVID research prioritization within a learning health system

Background

An integral component of research within a learning health system is patient engagement at all stages of the research process. While there are well-defined best practices for engaging with patients on predetermined research questions, there is little specific methodology for engaging patients at the stage of research question formation and prioritization. Further, with an emerging disease such as Long COVID, population-specific strategies for meaningful engagement have not been characterized.

Methods

The COVID-19 Focused Virtual Patient Engagement Studio (CoVIP studio) was a virtual panel created to facilitate patient-centered studies surrounding the effects of long-term COVID (“Long COVID”) also known as post-acute SARS-CoV-2 syndrome (PASC). A diverse group of panelists was recruited and trained in several different areas of knowledge, competencies, and abilities regarding research and Long COVID. A three-step approach was developed that consisted of recording panelists' broad wonderings to generate patient-specific research questions.

Results

The “wonderings” discussed in panelists' training sessions were analyzed to identify specific populations, interventions, comparators, outcomes, and timeframes (PICOT) elements, which were then used to create a survey to identify the elements of greatest importance to the panel. Based on the findings, 10 research questions were formulated using the PICOT format. The panelists then ranked the questions on perceived order of importance and distributed one million fictional grant dollars between the five chosen questions in the second survey. Through this stepwise prioritization process, the project team successfully translated panelists' research wonderings into investigable research questions.

Conclusion

This methodology has implications for the advancement of patient-engaged prioritization both within the scope of Long COVID research and in research on other rare or emerging diseases.

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来源期刊
Learning Health Systems
Learning Health Systems HEALTH POLICY & SERVICES-
CiteScore
5.60
自引率
22.60%
发文量
55
审稿时长
20 weeks
期刊最新文献
Issue Information Envisioning public health as a learning health system Thanks to our peer reviewers Learning health systems to implement chronic disease prevention programs: A novel framework and perspectives from an Australian health service The translation-to-policy learning cycle to improve public health
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